Healthcare professionals need to keep themselves updated with the latest medical developments by finding and reading relevant articles in order to provide the best possible care to their patients. The most popular technique for retrieving relevant articles from a digital library is keyword matching, which is known to retrieve a large amount of irrelevant articles without taking into account the...

Rapid increase in the amount of audio data and especially music collections demand an efficient method to automatically retrieve audio objects based on its content. In this paper, based on the Gabor wavelet features, we will propose a method for content-based retrieval of perceptually similar music pieces in audio documents. It allows the user to select a reference passage within an audio file ...

Content-based and collaborative filtering methods are the most successful solutions in recommender systems. Content-based method is based on item’s attributes. This method checks the features of user's favourite items and then proposes the items which have the most similar characteristics with those items. Collaborative filtering method is based on the determination of similar items or similar ...

News recommendation has become a big attraction with which major Web search portals retain their users. Two effective approaches are Content-based Filtering and Collaborative Filtering, each serving a specific recommendation scenario. The Content-based Filtering approaches inspect rich contexts of the recommended items, while the Collaborative Filtering approaches predict the interests of long-...

In music search and recommendation methods used in the present time, a general filtering method that obtains a result by inquiring music information and recommends a music list using users’ profiles is used. However, this filtering method presents a certain difficulty to obtain users’ information according to their circumstances because it only considers users’ static information, such as perso...

With the flooding of pornographic information on the Internet, how to keep people away from that offensive information is becoming one of the most important research areas in network information security. Some applications which can block or filter such information are used. Approaches in those systems can be roughly classified into two kinds: metadata based and content based. With the developm...

To overcome data sparsity problem, we propose a cross domain recommendation system named CCCFNet which can combine collaborative filtering and content-based filtering in a unified framework. We first introduce a factorization framework to tie CF and content-based filtering together. Then we find that the MAP estimation of this framework can be embedded into a multi-view neural network. Through ...

The paper presents a recommendation-based approach for knowledge resources in Communities of Practice of E-learning (CoPEs). The proposed approach is based on the hybrid semantic information filtering (IF), integrating the content-based filtering, the collaborative filtering and the ontology-based filtering approaches. The main idea is to apply a multi-level filtering, where three dimensions ha...

Spam messages are an increasing threat to mobile communication. Several mitigation techniques have been proposed, including white and black listing, challenge-response and content-based filtering. However, none are perfect and it makes sense to use a combination rather than just one. We propose an anti-spam framework based on the hybrid of contentbased filtering and challenge-response. A messag...

Machine Learning algorithms have a variety of important applications, and among them, Recommender systems are crucial. The internet hosts an extensive volume information, making it challenging for users to navigate find relevant content. therefore emerged as valuable tools bridge this gap. They facilitate the connection between content by offering personalized recommendations. In recent years r...